Identification of tumor epithelium and stroma in tissue microarrays using texture analysis

• Published in: Diagnostic pathology Vol. 7; no. 1; p. 22
• Main Authors: Linder, Nina, Konsti, Juho, Turkki, Riku, Rahtu, Esa, Lundin, Mikael, Nordling, Stig, Haglund, Caj, Ahonen, Timo, Pietikäinen, Matti, Lundin, Johan
• Format: Journal Article
• Language: English
• Published: London BioMed Central 02.03.2012; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Accuracy; Algorithms; Area Under Curve; Automation; Classification; Colorectal cancer; Colorectal Neoplasms - pathology; Diagnosis; Epithelium; Epithelium - pathology; Extracellular Matrix - pathology; Gabor; Haralick; Humans; Image analysis; Image Interpretation, Computer-Assisted - methods; Local binary patterns; Medicine; Medicine & Public Health; Pathology; Pattern recognition; Physiological aspects; ROC Curve; Sensitivity and Specificity; Statistical methods; Stroma; Studies; Support Vector Machine; Texture classification; Tissue Array Analysis - methods; Tissue microarrays; Finland; Image analysis; Local binary patterns; Stroma; Texture classification; Pattern recognition; Support vector machine; Epithelium; Gabor; Haralick
• ISSN: 1746-1596; 1746-1596
• DOI: 10.1186/1746-1596-7-22

Background The aim of the study was to assess whether texture analysis is feasible for automated identification of epithelium and stroma in digitized tumor tissue microarrays (TMAs). Texture analysis based on local binary patterns (LBP) has previously been used successfully in applications such as face recognition and industrial machine vision. TMAs with tissue samples from 643 patients with colorectal cancer were digitized using a whole slide scanner and areas representing epithelium and stroma were annotated in the images. Well-defined images of epithelium (n = 41) and stroma (n = 39) were used for training a support vector machine (SVM) classifier with LBP texture features and a contrast measure C (LBP/C) as input. We optimized the classifier on a validation set (n = 576) and then assessed its performance on an independent test set of images (n = 720). Finally, the performance of the LBP/C classifier was evaluated against classifiers based on Haralick texture features and Gabor filtered images. Results The proposed approach using LPB/C texture features was able to correctly differentiate epithelium from stroma according to texture: the agreement between the classifier and the human observer was 97 per cent (kappa value = 0.934, P < 0.0001) and the accuracy (area under the ROC curve) of the LBP/C classifier was 0.995 (CI95% 0.991-0.998). The accuracy of the corresponding classifiers based on Haralick features and Gabor-filter images were 0.976 and 0.981 respectively. Conclusions The method illustrates the capability of automated segmentation of epithelial and stromal tissue in TMAs based on texture features and an SVM classifier. Applications include tissue specific assessment of gene and protein expression, as well as computerized analysis of the tumor microenvironment. Virtual slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/4123422336534537